Session 2, Reading 9 (Part 2): This video reviews portfolio variance and covariance, where covariance is the expected cross-product. We look at correlation, which is given by the covariance divided by the product of standard deviations, and therefore standardizes the covariance into a unitless...
Covariance is a measure of linear co-movement between variables. Independence implies zero covariance, but the converse is not necessarily true (because variables can be dependent in a non-linear way).
Here is David's XLS: http://trtl.bz/2B9nqdO
Variables are independent if and only if (iff) their JOINT probability is equal to the product of their unconditional (aka, marginal) probabilities; i.e., if and only if Prob(X,Y) = Prob(X)*Prob(Y). Further, if variables are independent then their covariance (and correlation) is equal to zero...
Learning objectives: Calculate and interpret the covariance and correlation between two random variables. Calculate the mean and variance of sums of variables.
Questions:
711.1. The following probability matrix displays joint probabilities for an inflation outcome, I = {2, 3, or 4}, and an...
I was looking at this specific 2-asset portfolio example and noticed that BT uses the matrix formula to get the variance of P.
What I'm confused about is why do you not use the variance formula: variance = X1^2*stddev(asset1)^2 + X2^2*stddev(asset2)^2 +...
Learning objectives: Calculate covariance using the EWMA and GARCH(1,1) models. Apply the consistency condition to covariance. Describe the procedure of generating samples from a bivariate normal distribution. Describe properties of correlations between normally distributed variables when using...
Learning objective: Define correlation and covariance and differentiate between correlation and dependence.
Questions:
705.1. In order to evaluate the the potential of a linear relationship between portfolio returns and a benchmark index, your colleague Richard conducted a univariate...
Learning outcomes: Define covariance stationary, autocovariance function, autocorrelation function, partial autocorrelation function and autoregression. Describe the requirements for a series to be covariance stationary. Explain the implications of working with models that are not covariance...
Learning outcomes: Define correlation and covariance, differentiate between correlation and dependence. Calculate covariance using the EWMA and GARCH (1,1) models. Apply the consistency condition to covariance.
Questions:
502.1. About the consistency condition, each of the following is true...
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